A Stratified Feature Ranking Method for Supervised Feature Selection

Most feature selection methods usually select the highest rank features which may be highly correlated with each other. In this paper, we propose a Stratified Feature Ranking (SFR) method for supervised feature selection. In the new method, a Subspace Feature Clustering (SFC) is proposed to identify feature clusters, and a stratified feature ranking method is proposed to rank the features such that the high rank features are lowly correlated. Experimental results show the superiority of SFR.

Authors

• Renjie Chen
• Xiaojun Chen Shenzhen University
• Guowen Yuan University of Hong Kong
• Wenya Sun
• Qingyao Wu South China University of Technology Peng Cheng Laboratory

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